Title: Data Science for ERP Software Developers

Data Science for ERP Software Developers Book

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Table of Contents

The objective of this book is to teach the Data Science for ERP Software Developers using python, tensorflow and keras.

Chapter 1: Introduction to Data science

  1. Why this book ?
  2. What is Data Science? Getting value out of data
  3. Skills necessary for a Data Scientist? Why Python for Data Science?
  4. Why Python?
  5. Case Study: Soccer Data Analysis
  6. Steps in Data Science process
  7. Importance of defining the well stated data science problem
  8. Steps in data science in detail
  9. Acquiring data
  10. Exploring Data
  11. Pre-Processing Data
  12. Scaling and Transformation
  13. Dimensionality reduction
  14. Data manipulation
  15. Analyze Data
  16. Classification
  17. Regression
  18. Clustering
  19. Association analysis
  20. Reporting Insights
  21. Turning Insights into Action

Chapter 2: Python Programming for Data science

  1. Install Python on Windows using Anaconda Python Distribution
  2. Why Jupyter Notebook?
  3. Python Variables
  4. Python Loops
  5. for Loop
  6. While Loop
  7. Python conditions
  8. Relational Operators
  9. Logical Operators
  10. Data Structures - lists
  11. Indexing Lists
  12. Iterating over a list
  13. Modifying Items in Lists
  14. Appending to a list
  15. Removing from a list
  16. Merging two lists using Extend method
  17. Iterate over Multiple Lists Simultaneously
  18. Applying Functions To List Items
  19. List slicing
  20. Constructing a List with List Items
  21. String functions
  22. Concatenation, Strip, Split, Slicing
  23. Find a substring
  24. String Formatting
  25. Tuples in Python
  26. Creating a tuple
  27. Accessing Values in Tuples
  28. Dictionaries
  29. Accessing Dictionary Elements with Keys
  30. Using Functions to Access Elements
  31. Modifying Dictionaries
  32. Remove items from a Dictionary
  33. Numpy Library
  34. Introduction to Arrays
  35. Create a rank one ndarray
  36. Two-dimensional arrays
  37. Create a rank two numpy array
  38. Summing the Values in an Array
  39. Indexing and Slice indexing
  40. Using combinations of integer indexing and slice indexing to create different shaped matrices
  41. Numpy: ndarray boolean indexing
  42. Numpy: ndarray Datatypes and Operations
  43. Arithmetic Array Operations
  44. Numpy Statistical Operations
  45. Matrix Computations
  46. ndarrays broadcasting
  47. Broadcasting rules and Example

Chapter 3: Pandas -Data Analysis Library

  1. Introduction to Pandas Library
  2. Pandas Data Structures
  3. Pandas Series
  4. Pandas DataFrame
  5. Differences between ndarrays and Series Objects
  6. Indexing and Slicing Series
  7. Integer-location based indexing Example
  8. Series Initialized with Dictionaries
  9. DataFrames
  10. Example 1:- Create a Dataframe from a Dictionary of equal length lists with columns attribute
  11. Example 2:- Create a dataframe from a dictionary of equal length lists with index attribute
  12. Example 3 :- Create DataFrame from dictionary of Pandas Series
  13. Example 4:- Create DataFrame from Pandas Series
  14. Example 5 :- Sort the DataFrame's rows in descending order
  15. Example 6 :- Selection and Indexing Methods for Pandas DataFrames
  16. 1. Selecting pandas data using "iloc"
  17. 2. Selecting pandas data using "loc"
  18. 2a. Label-based / Index-based indexing using .loc
  19. 2b. Boolean / Logical indexing using .loc
  20. Example 7 :- Setting values in DataFrames using .loc
  21. Data Cleaning with Pandas
  22. Handling Data Quality Issues
  23. Interpolation of the data
  24. Handling Missing Values
  25. Drop fields using dropna() function
  26. Drop rows with all cells having missing values
  27. Drop rows with any cell having missing values
  28. Drop column containing all missing Values
  29. Replace function
  30. Checks for any row is Null
  31. Fill missing data gaps forward and backward
  32. Fill in missing data with zeros
  33. Fill in missing in preTestScore with the mean value of preTestScore
  34. Pandas: Data Ingestion
  35. Loading CSV into Pandas DataFrame
  36. Loading a JSON file into Pandas DataFrame
  37. Join And Merge Pandas Dataframe
  38. The concat function
  39. Another example for Concatenation
  40. Merge with outer join
  41. Merge with inner join
  42. Merge with right join
  43. Merge with left join
  44. Statistical Analysis with DataFrames
  45. Summarizing, Aggregating, and Grouping data in Python Pandas
  46. Grouping by multiple variables
  47. Multiple Statistics per Group
  48. Data Visualization in Python
  49. Introduction to Matplotlib
  50. Line Graph
  51. Scatter plot
  52. Making A Matplotlib Scatterplot From A Pandas Dataframe

Chapter 4: ML algorithms and logic behind it

  1. Introduction to Machine Learning
  2. What is Machine Learning and main applications of Machine Learning? What does actually Machine(model or statistical model) learn?
  3. Concepts in Machine Learning
  4. What is a statistical model?
  5. What exactly is being learnt in machine learning?
  6. What are Inputs to Statistical models?
  7. Model selection
  8. Common Machine Learning use cases
  9. Categories of Machine Learning
  10. Classification
  11. Regression
  12. Cluster Analysis
  13. Association analysis
  14. Terminology Related to Machine Learning
  15. Classification in detail
  16. Binary classification
  17. Multi-class classification
  18. Building and Applying a Classification Model
  19. What is a Machine Learning Model?
  20. Building Machine Learning Model
  21. Building Classification Model
  22. Training phase
  23. Testing phase
  24. Algorithms to build a classification model
  25. K-nearest Neighbors Classification
  26. Knn classification application
  27. Understand the knn algorithm using the example
  28. Similarity distance measures
  29. Code and explanation from notebook
  30. Another Example for Classification
  31. Decision Trees
  32. How does a decision tree determine the best way to split the set of samples at a node?
  33. Tree Induction Example
  34. Decision boundaries
  35. Decision Trees Example code in the notebook with explanation